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The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

The materials and energy density of current electric vehicles (EV) battery technology means that the vehicles are heavier and have a shorter range in comparison to internal combustion engine vehicles (ICEV). Battery cost also means EVs are relatively expensive for the consumer, even with government incentives, and dependent on sometimes-rare resources being available. These factors also limit the applicability of battery-electric technologies to heavy-duty vehicles. However, a number of next generation technologies are under laboratory development which could radically change this situation. Using a follow-the-money methodology, the strategic innovations of companies and public institutions are examined. The chapter will review the potential for changes in resource inputs, higher-density batteries and cost reductions, considering options such as lithium-air, metal-air and solid-state technologies. The innovations outlined in these technologies are considered from an economic perspective, identifying their advantages and disadvantages in commercialisation. At the same time, innovations, and investments in infrastructure electrification (Electric Road Service) and battery exchange point with swapping technology will be also considered due their implications and contribution to solving battery-related challenges and shortcomings. It is concluded that only a joint investment in effort on technologies would allow the use of EVs to be extended to a broad public in terms both of users and geography.

Heavy metals play a crucial role in the economic development of any nation. Industries utilizing heavy metals, consequently, emanate a large volume of metal-containing liquid effluents. Since metals are non-renewable and finite resources, their judicious and sustainable use is the key. Hazardous metal-laden water poses threat to human health and ecology. Apart from metals, these industrial effluents also consist of toxic chemicals. Conventional physical–chemical techniques are not efficient enough as it consumes energy and are, therefore, not cost effective.

It is known that biomaterials namely microorganisms, plants, and agricultural biomass have the competence to bind metals, in some cases, selectively, from aqueous medium. This phenomenon is termed as “metal biosorption.” Biosorption has immense potential of becoming an effective alternative over conventional methods. The authors in the present chapter have used secondary data from their previous research work and attempted to develop few strategic models through their feasibility studies for metal sustainability.